Overview

Dataset statistics

Number of variables27
Number of observations9132
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory216.0 B

Variable types

Categorical7
Text11
Numeric9

Alerts

is_remote is highly imbalanced (81.7%)Imbalance

Reproduction

Analysis started2024-07-06 05:53:52.252549
Analysis finished2024-07-06 05:54:08.993366
Duration16.74 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Job_role
Categorical

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Software Developer
1065 
Software Engineer
922 
Web Developer
812 
Data Analyst
762 
Data Scientist
540 
Other values (27)
5031 

Length

Max length30
Median length21
Mean length15.318441
Min length7

Characters and Unicode

Total characters139888
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData Analyst
2nd rowData Analyst
3rd rowData Analyst
4th rowData Analyst
5th rowData Analyst

Common Values

ValueCountFrequency (%)
Software Developer 1065
 
11.7%
Software Engineer 922
 
10.1%
Web Developer 812
 
8.9%
Data Analyst 762
 
8.3%
Data Scientist 540
 
5.9%
Web Designer 461
 
5.0%
Digital Marketing Executive 361
 
4.0%
Sr. Data Analyst 324
 
3.5%
Content Writer 303
 
3.3%
Associate 284
 
3.1%
Other values (22) 3298
36.1%

Length

2024-07-06T11:24:09.121259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
developer 2251
12.1%
data 2244
12.1%
software 2183
11.7%
engineer 1586
 
8.5%
executive 1463
 
7.9%
analyst 1432
 
7.7%
web 1273
 
6.8%
scientist 688
 
3.7%
sr 668
 
3.6%
business 536
 
2.9%
Other values (22) 4287
23.0%

Most occurring characters

ValueCountFrequency (%)
e 21765
15.6%
t 11979
 
8.6%
a 10399
 
7.4%
9479
 
6.8%
r 9111
 
6.5%
n 8192
 
5.9%
i 7378
 
5.3%
o 6230
 
4.5%
D 5664
 
4.0%
l 5163
 
3.7%
Other values (31) 44528
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 110368
78.9%
Uppercase Letter 18960
 
13.6%
Space Separator 9479
 
6.8%
Other Punctuation 859
 
0.6%
Connector Punctuation 222
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21765
19.7%
t 11979
10.9%
a 10399
9.4%
r 9111
8.3%
n 8192
 
7.4%
i 7378
 
6.7%
o 6230
 
5.6%
l 5163
 
4.7%
s 5011
 
4.5%
v 3984
 
3.6%
Other values (13) 21156
19.2%
Uppercase Letter
ValueCountFrequency (%)
D 5664
29.9%
S 4091
21.6%
E 3187
16.8%
A 1845
 
9.7%
W 1576
 
8.3%
M 645
 
3.4%
C 619
 
3.3%
B 536
 
2.8%
O 257
 
1.4%
R 234
 
1.2%
Other values (4) 306
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 748
87.1%
/ 111
 
12.9%
Space Separator
ValueCountFrequency (%)
9479
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129328
92.5%
Common 10560
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21765
16.8%
t 11979
 
9.3%
a 10399
 
8.0%
r 9111
 
7.0%
n 8192
 
6.3%
i 7378
 
5.7%
o 6230
 
4.8%
D 5664
 
4.4%
l 5163
 
4.0%
s 5011
 
3.9%
Other values (27) 38436
29.7%
Common
ValueCountFrequency (%)
9479
89.8%
. 748
 
7.1%
_ 222
 
2.1%
/ 111
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21765
15.6%
t 11979
 
8.6%
a 10399
 
7.4%
9479
 
6.8%
r 9111
 
6.5%
n 8192
 
5.9%
i 7378
 
5.3%
o 6230
 
4.5%
D 5664
 
4.0%
l 5163
 
3.7%
Other values (31) 44528
31.8%
Distinct5095
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:09.511278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length60
Mean length16.403417
Min length1

Characters and Unicode

Total characters149796
Distinct characters82
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3654 ?
Unique (%)40.0%

Sample

1st rowOutscal Technologies
2nd rowRedbus
3rd rowAmiha Agro
4th rowA K Infra Reality Developers
5th rowBolla Management
ValueCountFrequency (%)
solutions 643
 
3.2%
technologies 630
 
3.1%
ltd 434
 
2.1%
services 409
 
2.0%
pvt 395
 
2.0%
software 265
 
1.3%
india 246
 
1.2%
accenture 228
 
1.1%
consulting 220
 
1.1%
infotech 196
 
1.0%
Other values (5841) 16587
81.9%
2024-07-06T11:24:10.056710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 13434
 
9.0%
11147
 
7.4%
n 9846
 
6.6%
o 9802
 
6.5%
i 9569
 
6.4%
t 9173
 
6.1%
a 8507
 
5.7%
s 7497
 
5.0%
r 6943
 
4.6%
l 5775
 
3.9%
Other values (72) 58103
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111723
74.6%
Uppercase Letter 25108
 
16.8%
Space Separator 11147
 
7.4%
Other Punctuation 962
 
0.6%
Decimal Number 414
 
0.3%
Close Punctuation 166
 
0.1%
Open Punctuation 166
 
0.1%
Dash Punctuation 100
 
0.1%
Math Symbol 5
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13434
12.0%
n 9846
 
8.8%
o 9802
 
8.8%
i 9569
 
8.6%
t 9173
 
8.2%
a 8507
 
7.6%
s 7497
 
6.7%
r 6943
 
6.2%
l 5775
 
5.2%
c 5211
 
4.7%
Other values (16) 25966
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 3430
13.7%
I 2263
 
9.0%
T 2229
 
8.9%
C 2001
 
8.0%
A 1791
 
7.1%
P 1505
 
6.0%
L 1384
 
5.5%
M 1261
 
5.0%
E 1248
 
5.0%
D 1060
 
4.2%
Other values (16) 6936
27.6%
Other Punctuation
ValueCountFrequency (%)
. 639
66.4%
& 140
 
14.6%
; 85
 
8.8%
, 73
 
7.6%
/ 12
 
1.2%
@ 5
 
0.5%
' 3
 
0.3%
: 3
 
0.3%
\ 1
 
0.1%
% 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 97
23.4%
7 78
18.8%
4 49
11.8%
0 47
11.4%
6 30
 
7.2%
1 28
 
6.8%
3 26
 
6.3%
5 24
 
5.8%
9 21
 
5.1%
8 14
 
3.4%
Close Punctuation
ValueCountFrequency (%)
) 165
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 165
99.4%
[ 1
 
0.6%
Math Symbol
ValueCountFrequency (%)
+ 3
60.0%
| 2
40.0%
Space Separator
ValueCountFrequency (%)
11147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136831
91.3%
Common 12965
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13434
 
9.8%
n 9846
 
7.2%
o 9802
 
7.2%
i 9569
 
7.0%
t 9173
 
6.7%
a 8507
 
6.2%
s 7497
 
5.5%
r 6943
 
5.1%
l 5775
 
4.2%
c 5211
 
3.8%
Other values (42) 51074
37.3%
Common
ValueCountFrequency (%)
11147
86.0%
. 639
 
4.9%
) 165
 
1.3%
( 165
 
1.3%
& 140
 
1.1%
- 100
 
0.8%
2 97
 
0.7%
; 85
 
0.7%
7 78
 
0.6%
, 73
 
0.6%
Other values (20) 276
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149795
> 99.9%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13434
 
9.0%
11147
 
7.4%
n 9846
 
6.6%
o 9802
 
6.5%
i 9569
 
6.4%
t 9173
 
6.1%
a 8507
 
5.7%
s 7497
 
5.0%
r 6943
 
4.6%
l 5775
 
3.9%
Other values (71) 58102
38.8%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

job_pay
Real number (ℝ)

Distinct7391
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean772692.83
Minimum46093.75
Maximum15750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:10.460408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46093.75
5-th percentile257239.9
Q1496791.28
median695397.63
Q3944418.71
95-th percentile1478020.5
Maximum15750000
Range15703906
Interquartile range (IQR)447627.43

Descriptive statistics

Standard deviation487425.85
Coefficient of variation (CV)0.63081452
Kurtosis148.23838
Mean772692.83
Median Absolute Deviation (MAD)221351.64
Skewness6.8871465
Sum7.0562309 × 109
Variance2.3758396 × 1011
MonotonicityNot monotonic
2024-07-06T11:24:10.614325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 74
 
0.8%
325000 72
 
0.8%
450000 63
 
0.7%
300000 60
 
0.7%
343673.5147 58
 
0.6%
400000 51
 
0.6%
250000 49
 
0.5%
275000 46
 
0.5%
550000 41
 
0.4%
500000 37
 
0.4%
Other values (7381) 8581
94.0%
ValueCountFrequency (%)
46093.75 1
 
< 0.1%
50000 1
 
< 0.1%
55000 1
 
< 0.1%
60000 4
< 0.1%
65000 2
< 0.1%
66250 1
 
< 0.1%
68750 2
< 0.1%
75000 3
< 0.1%
76562.5 1
 
< 0.1%
82812.5 1
 
< 0.1%
ValueCountFrequency (%)
15750000 1
< 0.1%
12750000 1
< 0.1%
6914741.071 1
< 0.1%
4660416.667 1
< 0.1%
4447596.822 1
< 0.1%
4252203.621 1
< 0.1%
4250000 1
< 0.1%
4235628.912 1
< 0.1%
4186134.595 1
< 0.1%
4170214.782 1
< 0.1%
Distinct1048
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:10.914038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length130
Median length106
Mean length13.273544
Min length4

Characters and Unicode

Total characters121214
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique719 ?
Unique (%)7.9%

Sample

1st rowRemote
2nd rowBangalore Rural
3rd rowAhmedabad(Shyamal Cross Road)
4th rowLucknow
5th rowHyderabad(Kondapur)
ValueCountFrequency (%)
bengaluru 1876
11.8%
delhi 1600
 
10.0%
mumbai 1271
 
8.0%
new 1240
 
7.8%
gurugram 1203
 
7.5%
noida 1046
 
6.6%
pune 965
 
6.1%
hyderabad 849
 
5.3%
chennai 805
 
5.1%
kolkata 510
 
3.2%
Other values (707) 4575
28.7%
2024-07-06T11:24:11.425048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15140
 
12.5%
u 10368
 
8.6%
e 10042
 
8.3%
r 8306
 
6.9%
6810
 
5.6%
i 6588
 
5.4%
n 6195
 
5.1%
l 5383
 
4.4%
d 4952
 
4.1%
, 4340
 
3.6%
Other values (59) 43090
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91757
75.7%
Uppercase Letter 16472
 
13.6%
Space Separator 6810
 
5.6%
Other Punctuation 4742
 
3.9%
Open Punctuation 531
 
0.4%
Close Punctuation 531
 
0.4%
Dash Punctuation 161
 
0.1%
Decimal Number 153
 
0.1%
Math Symbol 57
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15140
16.5%
u 10368
11.3%
e 10042
10.9%
r 8306
9.1%
i 6588
 
7.2%
n 6195
 
6.8%
l 5383
 
5.9%
d 4952
 
5.4%
g 4011
 
4.4%
h 3892
 
4.2%
Other values (15) 16880
18.4%
Uppercase Letter
ValueCountFrequency (%)
N 2555
15.5%
B 2414
14.7%
D 1708
10.4%
G 1555
9.4%
M 1520
9.2%
H 1223
7.4%
P 1164
7.1%
C 1059
6.4%
K 771
 
4.7%
A 713
 
4.3%
Other values (15) 1790
10.9%
Decimal Number
ValueCountFrequency (%)
1 45
29.4%
2 32
20.9%
6 16
 
10.5%
0 16
 
10.5%
3 14
 
9.2%
5 11
 
7.2%
4 10
 
6.5%
8 4
 
2.6%
9 3
 
2.0%
7 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 4340
91.5%
/ 395
 
8.3%
. 4
 
0.1%
& 3
 
0.1%
Space Separator
ValueCountFrequency (%)
6810
100.0%
Open Punctuation
ValueCountFrequency (%)
( 531
100.0%
Close Punctuation
ValueCountFrequency (%)
) 531
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 161
100.0%
Math Symbol
ValueCountFrequency (%)
+ 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108229
89.3%
Common 12985
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15140
14.0%
u 10368
 
9.6%
e 10042
 
9.3%
r 8306
 
7.7%
i 6588
 
6.1%
n 6195
 
5.7%
l 5383
 
5.0%
d 4952
 
4.6%
g 4011
 
3.7%
h 3892
 
3.6%
Other values (40) 33352
30.8%
Common
ValueCountFrequency (%)
6810
52.4%
, 4340
33.4%
( 531
 
4.1%
) 531
 
4.1%
/ 395
 
3.0%
- 161
 
1.2%
+ 57
 
0.4%
1 45
 
0.3%
2 32
 
0.2%
6 16
 
0.1%
Other values (9) 67
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121213
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15140
 
12.5%
u 10368
 
8.6%
e 10042
 
8.3%
r 8306
 
6.9%
6810
 
5.6%
i 6588
 
5.4%
n 6195
 
5.1%
l 5383
 
4.4%
d 4952
 
4.1%
, 4340
 
3.6%
Other values (58) 43089
35.5%
None
ValueCountFrequency (%)
 1
100.0%

review
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.810184
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:11.595371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q13.5
median4
Q34.3
95-th percentile4.9
Maximum5
Range4
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.76833575
Coefficient of variation (CV)0.20165319
Kurtosis1.7893354
Mean3.810184
Median Absolute Deviation (MAD)0.4
Skewness-1.1316837
Sum34794.6
Variance0.59033982
MonotonicityNot monotonic
2024-07-06T11:24:11.740763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4 1039
 
11.4%
4.1 671
 
7.3%
4.2 584
 
6.4%
3.9 530
 
5.8%
3.8 526
 
5.8%
4.3 477
 
5.2%
3.7 455
 
5.0%
5 437
 
4.8%
3.6 427
 
4.7%
4.4 377
 
4.1%
Other values (31) 3609
39.5%
ValueCountFrequency (%)
1 49
0.5%
1.1 25
0.3%
1.2 34
0.4%
1.3 34
0.4%
1.4 17
 
0.2%
1.5 27
0.3%
1.6 20
0.2%
1.7 29
0.3%
1.8 28
0.3%
1.9 33
0.4%
ValueCountFrequency (%)
5 437
4.8%
4.9 154
 
1.7%
4.8 202
 
2.2%
4.7 263
 
2.9%
4.6 189
 
2.1%
4.5 284
3.1%
4.4 377
4.1%
4.3 477
5.2%
4.2 584
6.4%
4.1 671
7.3%

Job post length
Real number (ℝ)

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.848555
Minimum3
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:11.900836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12
Q116
median22
Q332
95-th percentile53.45
Maximum70
Range67
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.875313
Coefficient of variation (CV)0.49810573
Kurtosis1.5108794
Mean25.848555
Median Absolute Deviation (MAD)7
Skewness1.3187415
Sum236049
Variance165.77369
MonotonicityNot monotonic
2024-07-06T11:24:12.065303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 596
 
6.5%
13 572
 
6.3%
14 502
 
5.5%
17 471
 
5.2%
16 411
 
4.5%
12 400
 
4.4%
25 354
 
3.9%
19 351
 
3.8%
20 322
 
3.5%
22 289
 
3.2%
Other values (56) 4864
53.3%
ValueCountFrequency (%)
3 4
 
< 0.1%
6 2
 
< 0.1%
7 19
 
0.2%
8 6
 
0.1%
9 33
 
0.4%
10 42
 
0.5%
11 91
 
1.0%
12 400
4.4%
13 572
6.3%
14 502
5.5%
ValueCountFrequency (%)
70 52
0.6%
69 16
 
0.2%
68 33
0.4%
67 41
0.4%
66 19
 
0.2%
65 25
0.3%
64 21
0.2%
63 16
 
0.2%
62 24
0.3%
61 35
0.4%

min_year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2
4419 
1
3202 
0
1511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9132
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 4419
48.4%
1 3202
35.1%
0 1511
 
16.5%

Length

2024-07-06T11:24:12.226447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T11:24:12.421497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 4419
48.4%
1 3202
35.1%
0 1511
 
16.5%

Most occurring characters

ValueCountFrequency (%)
2 4419
48.4%
1 3202
35.1%
0 1511
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9132
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4419
48.4%
1 3202
35.1%
0 1511
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4419
48.4%
1 3202
35.1%
0 1511
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4419
48.4%
1 3202
35.1%
0 1511
 
16.5%

max_year
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4250986
Minimum0
Maximum30
Zeros54
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:12.536095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6878186
Coefficient of variation (CV)0.38141943
Kurtosis9.3456492
Mean4.4250986
Median Absolute Deviation (MAD)1
Skewness1.1700083
Sum40410
Variance2.8487315
MonotonicityNot monotonic
2024-07-06T11:24:12.713354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 2064
22.6%
3 1935
21.2%
4 1893
20.7%
6 1175
12.9%
2 963
10.5%
7 910
10.0%
8 63
 
0.7%
0 54
 
0.6%
10 38
 
0.4%
9 11
 
0.1%
Other values (8) 26
 
0.3%
ValueCountFrequency (%)
0 54
 
0.6%
2 963
10.5%
3 1935
21.2%
4 1893
20.7%
5 2064
22.6%
6 1175
12.9%
7 910
10.0%
8 63
 
0.7%
9 11
 
0.1%
10 38
 
0.4%
ValueCountFrequency (%)
30 1
 
< 0.1%
20 2
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 9
 
0.1%
13 3
 
< 0.1%
12 7
 
0.1%
11 2
 
< 0.1%
10 38
0.4%
9 11
 
0.1%

more_branch
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7947876
Minimum0
Maximum13
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:12.861126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7469855
Coefficient of variation (CV)0.97336618
Kurtosis6.7652723
Mean1.7947876
Median Absolute Deviation (MAD)0
Skewness2.6865869
Sum16390
Variance3.0519584
MonotonicityNot monotonic
2024-07-06T11:24:13.022728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 6529
71.5%
2 1164
 
12.7%
3 485
 
5.3%
8 388
 
4.2%
4 195
 
2.1%
5 194
 
2.1%
6 96
 
1.1%
7 52
 
0.6%
9 13
 
0.1%
11 8
 
0.1%
Other values (3) 8
 
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 6529
71.5%
2 1164
 
12.7%
3 485
 
5.3%
4 195
 
2.1%
5 194
 
2.1%
6 96
 
1.1%
7 52
 
0.6%
8 388
 
4.2%
9 13
 
0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
11 8
 
0.1%
10 4
 
< 0.1%
9 13
 
0.1%
8 388
4.2%
7 52
 
0.6%
6 96
 
1.1%
5 194
 
2.1%
4 195
2.1%
3 485
5.3%

+3_branches
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
0
7694 
1
1438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9132
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Length

2024-07-06T11:24:13.131239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T11:24:13.289813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9132
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

days
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.58191
Minimum0
Maximum30
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:13.431034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median14
Q330
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.294597
Coefficient of variation (CV)0.74144641
Kurtosis-1.8770031
Mean16.58191
Median Absolute Deviation (MAD)10
Skewness0.047275144
Sum151426
Variance151.15713
MonotonicityNot monotonic
2024-07-06T11:24:13.563576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
30 3695
40.5%
4 3157
34.6%
1 339
 
3.7%
5 200
 
2.2%
13 144
 
1.6%
7 138
 
1.5%
8 128
 
1.4%
6 116
 
1.3%
12 101
 
1.1%
15 91
 
1.0%
Other values (21) 1023
 
11.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 339
 
3.7%
2 62
 
0.7%
3 30
 
0.3%
4 3157
34.6%
5 200
 
2.2%
6 116
 
1.3%
7 138
 
1.5%
8 128
 
1.4%
9 27
 
0.3%
ValueCountFrequency (%)
30 3695
40.5%
29 63
 
0.7%
28 60
 
0.7%
27 75
 
0.8%
26 61
 
0.7%
25 54
 
0.6%
24 22
 
0.2%
23 32
 
0.4%
22 84
 
0.9%
21 59
 
0.6%
Distinct1561
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:13.894743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length227
Median length37
Mean length11.859286
Min length1

Characters and Unicode

Total characters108299
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)9.8%

Sample

1st rowdata analysis
2nd rowsql
3rd rowstatistical data analysis
4th rowdata visualization
5th rowvba coding
ValueCountFrequency (%)
data 876
 
5.8%
analysis 755
 
5.0%
science 466
 
3.1%
computer 465
 
3.1%
management 377
 
2.5%
development 317
 
2.1%
web 247
 
1.6%
skills 247
 
1.6%
c 209
 
1.4%
marketing 203
 
1.3%
Other values (1172) 11014
72.6%
2024-07-06T11:24:14.315678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11048
 
10.2%
a 10820
 
10.0%
n 8659
 
8.0%
i 8405
 
7.8%
s 7972
 
7.4%
t 7830
 
7.2%
6045
 
5.6%
c 5965
 
5.5%
o 5486
 
5.1%
r 5251
 
4.8%
Other values (29) 30818
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101811
94.0%
Space Separator 6045
 
5.6%
Other Punctuation 216
 
0.2%
Math Symbol 178
 
0.2%
Decimal Number 49
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11048
10.9%
a 10820
10.6%
n 8659
 
8.5%
i 8405
 
8.3%
s 7972
 
7.8%
t 7830
 
7.7%
c 5965
 
5.9%
o 5486
 
5.4%
r 5251
 
5.2%
l 5145
 
5.1%
Other values (16) 25230
24.8%
Decimal Number
ValueCountFrequency (%)
2 25
51.0%
3 11
22.4%
0 5
 
10.2%
1 3
 
6.1%
5 2
 
4.1%
6 2
 
4.1%
4 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
# 104
48.1%
. 97
44.9%
/ 12
 
5.6%
& 3
 
1.4%
Space Separator
ValueCountFrequency (%)
6045
100.0%
Math Symbol
ValueCountFrequency (%)
+ 178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 101811
94.0%
Common 6488
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11048
10.9%
a 10820
10.6%
n 8659
 
8.5%
i 8405
 
8.3%
s 7972
 
7.8%
t 7830
 
7.7%
c 5965
 
5.9%
o 5486
 
5.4%
r 5251
 
5.2%
l 5145
 
5.1%
Other values (16) 25230
24.8%
Common
ValueCountFrequency (%)
6045
93.2%
+ 178
 
2.7%
# 104
 
1.6%
. 97
 
1.5%
2 25
 
0.4%
/ 12
 
0.2%
3 11
 
0.2%
0 5
 
0.1%
& 3
 
< 0.1%
1 3
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11048
 
10.2%
a 10820
 
10.0%
n 8659
 
8.0%
i 8405
 
7.8%
s 7972
 
7.4%
t 7830
 
7.2%
6045
 
5.6%
c 5965
 
5.5%
o 5486
 
5.1%
r 5251
 
4.8%
Other values (29) 30818
28.5%
Distinct1776
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:14.703579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length176
Median length38
Mean length11.604468
Min length1

Characters and Unicode

Total characters105972
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1014 ?
Unique (%)11.1%

Sample

1st rowdata visualization
2nd rowmis reporting
3rd rowdata analytics
4th rowdata extraction
5th rowpower bi
ValueCountFrequency (%)
data 1016
 
6.8%
analysis 817
 
5.5%
web 478
 
3.2%
management 375
 
2.5%
development 301
 
2.0%
technologies 259
 
1.7%
c 217
 
1.4%
front 203
 
1.4%
end 199
 
1.3%
jquery 177
 
1.2%
Other values (1288) 10938
73.0%
2024-07-06T11:24:15.249922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11187
 
10.6%
e 10829
 
10.2%
n 8761
 
8.3%
t 7955
 
7.5%
i 7820
 
7.4%
s 7666
 
7.2%
5853
 
5.5%
o 5715
 
5.4%
l 5203
 
4.9%
r 5084
 
4.8%
Other values (32) 29899
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 99649
94.0%
Space Separator 5853
 
5.5%
Math Symbol 283
 
0.3%
Other Punctuation 121
 
0.1%
Decimal Number 65
 
0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11187
11.2%
e 10829
10.9%
n 8761
 
8.8%
t 7955
 
8.0%
i 7820
 
7.8%
s 7666
 
7.7%
o 5715
 
5.7%
l 5203
 
5.2%
r 5084
 
5.1%
c 4970
 
5.0%
Other values (16) 24459
24.5%
Decimal Number
ValueCountFrequency (%)
2 31
47.7%
3 11
 
16.9%
1 6
 
9.2%
0 5
 
7.7%
4 3
 
4.6%
9 3
 
4.6%
8 2
 
3.1%
5 2
 
3.1%
6 2
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 84
69.4%
# 24
 
19.8%
/ 7
 
5.8%
& 6
 
5.0%
Space Separator
ValueCountFrequency (%)
5853
100.0%
Math Symbol
ValueCountFrequency (%)
+ 283
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99649
94.0%
Common 6323
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11187
11.2%
e 10829
10.9%
n 8761
 
8.8%
t 7955
 
8.0%
i 7820
 
7.8%
s 7666
 
7.7%
o 5715
 
5.7%
l 5203
 
5.2%
r 5084
 
5.1%
c 4970
 
5.0%
Other values (16) 24459
24.5%
Common
ValueCountFrequency (%)
5853
92.6%
+ 283
 
4.5%
. 84
 
1.3%
2 31
 
0.5%
# 24
 
0.4%
3 11
 
0.2%
/ 7
 
0.1%
& 6
 
0.1%
1 6
 
0.1%
0 5
 
0.1%
Other values (6) 13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11187
 
10.6%
e 10829
 
10.2%
n 8761
 
8.3%
t 7955
 
7.5%
i 7820
 
7.4%
s 7666
 
7.2%
5853
 
5.5%
o 5715
 
5.4%
l 5203
 
4.9%
r 5084
 
4.8%
Other values (32) 29899
28.2%
Distinct1938
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:15.597649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length155
Median length39
Mean length11.713973
Min length1

Characters and Unicode

Total characters106972
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1117 ?
Unique (%)12.2%

Sample

1st rowtableau
2nd rowvlookup
3rd rowdata visualization
4th rowstatistics
5th rowadvance excel
ValueCountFrequency (%)
data 883
 
5.9%
web 612
 
4.1%
analysis 589
 
4.0%
management 454
 
3.1%
development 295
 
2.0%
technologies 279
 
1.9%
sql 234
 
1.6%
sales 183
 
1.2%
analytical 170
 
1.1%
coding 166
 
1.1%
Other values (1357) 11003
74.0%
2024-07-06T11:24:16.191932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11153
 
10.4%
a 10824
 
10.1%
n 8883
 
8.3%
i 7948
 
7.4%
t 7781
 
7.3%
s 7551
 
7.1%
o 5988
 
5.6%
5740
 
5.4%
l 5548
 
5.2%
r 5032
 
4.7%
Other values (32) 30524
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100942
94.4%
Space Separator 5740
 
5.4%
Math Symbol 136
 
0.1%
Other Punctuation 101
 
0.1%
Decimal Number 49
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11153
11.0%
a 10824
10.7%
n 8883
 
8.8%
i 7948
 
7.9%
t 7781
 
7.7%
s 7551
 
7.5%
o 5988
 
5.9%
l 5548
 
5.5%
r 5032
 
5.0%
c 4979
 
4.9%
Other values (16) 25255
25.0%
Decimal Number
ValueCountFrequency (%)
2 28
57.1%
3 8
 
16.3%
0 6
 
12.2%
4 3
 
6.1%
9 1
 
2.0%
1 1
 
2.0%
6 1
 
2.0%
5 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 79
78.2%
# 8
 
7.9%
/ 8
 
7.9%
& 6
 
5.9%
Space Separator
ValueCountFrequency (%)
5740
100.0%
Math Symbol
ValueCountFrequency (%)
+ 136
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100942
94.4%
Common 6030
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11153
11.0%
a 10824
10.7%
n 8883
 
8.8%
i 7948
 
7.9%
t 7781
 
7.7%
s 7551
 
7.5%
o 5988
 
5.9%
l 5548
 
5.5%
r 5032
 
5.0%
c 4979
 
4.9%
Other values (16) 25255
25.0%
Common
ValueCountFrequency (%)
5740
95.2%
+ 136
 
2.3%
. 79
 
1.3%
2 28
 
0.5%
3 8
 
0.1%
# 8
 
0.1%
/ 8
 
0.1%
0 6
 
0.1%
& 6
 
0.1%
4 3
 
< 0.1%
Other values (6) 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11153
 
10.4%
a 10824
 
10.1%
n 8883
 
8.3%
i 7948
 
7.4%
t 7781
 
7.3%
s 7551
 
7.1%
o 5988
 
5.6%
5740
 
5.4%
l 5548
 
5.2%
r 5032
 
4.7%
Other values (32) 30524
28.5%
Distinct1992
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:16.459187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length66
Median length33
Mean length11.617718
Min length1

Characters and Unicode

Total characters106093
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1136 ?
Unique (%)12.4%

Sample

1st rowpython
2nd rowdata analysis
3rd rowdata reporting
4th rowdata reporting
5th rowsql
ValueCountFrequency (%)
data 637
 
4.4%
web 498
 
3.4%
management 443
 
3.1%
development 302
 
2.1%
sql 287
 
2.0%
analysis 286
 
2.0%
analytical 259
 
1.8%
technologies 200
 
1.4%
learning 196
 
1.4%
javascript 191
 
1.3%
Other values (1350) 11188
77.2%
2024-07-06T11:24:16.957246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11016
 
10.4%
a 10529
 
9.9%
n 8715
 
8.2%
i 8244
 
7.8%
t 7702
 
7.3%
s 7015
 
6.6%
o 5862
 
5.5%
l 5539
 
5.2%
5357
 
5.0%
r 5258
 
5.0%
Other values (30) 30856
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100513
94.7%
Space Separator 5357
 
5.0%
Other Punctuation 103
 
0.1%
Decimal Number 64
 
0.1%
Math Symbol 56
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11016
11.0%
a 10529
10.5%
n 8715
 
8.7%
i 8244
 
8.2%
t 7702
 
7.7%
s 7015
 
7.0%
o 5862
 
5.8%
l 5539
 
5.5%
r 5258
 
5.2%
c 5194
 
5.2%
Other values (16) 25439
25.3%
Decimal Number
ValueCountFrequency (%)
2 34
53.1%
3 11
 
17.2%
0 7
 
10.9%
4 5
 
7.8%
5 3
 
4.7%
6 2
 
3.1%
8 1
 
1.6%
1 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 83
80.6%
# 9
 
8.7%
/ 8
 
7.8%
& 3
 
2.9%
Space Separator
ValueCountFrequency (%)
5357
100.0%
Math Symbol
ValueCountFrequency (%)
+ 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100513
94.7%
Common 5580
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11016
11.0%
a 10529
10.5%
n 8715
 
8.7%
i 8244
 
8.2%
t 7702
 
7.7%
s 7015
 
7.0%
o 5862
 
5.8%
l 5539
 
5.5%
r 5258
 
5.2%
c 5194
 
5.2%
Other values (16) 25439
25.3%
Common
ValueCountFrequency (%)
5357
96.0%
. 83
 
1.5%
+ 56
 
1.0%
2 34
 
0.6%
3 11
 
0.2%
# 9
 
0.2%
/ 8
 
0.1%
0 7
 
0.1%
4 5
 
0.1%
& 3
 
0.1%
Other values (4) 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11016
 
10.4%
a 10529
 
9.9%
n 8715
 
8.2%
i 8244
 
7.8%
t 7702
 
7.3%
s 7015
 
6.6%
o 5862
 
5.5%
l 5539
 
5.2%
5357
 
5.0%
r 5258
 
5.0%
Other values (30) 30856
29.1%
Distinct2037
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:17.270029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length74
Median length32
Mean length11.578077
Min length1

Characters and Unicode

Total characters105731
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1161 ?
Unique (%)12.7%

Sample

1st rowsql
2nd rowadvance excel
3rd rowreporting
4th rowanalytics
5th rowsql
ValueCountFrequency (%)
data 571
 
4.0%
web 466
 
3.3%
management 446
 
3.1%
development 387
 
2.7%
javascript 297
 
2.1%
analytical 252
 
1.8%
sql 248
 
1.7%
learning 214
 
1.5%
machine 211
 
1.5%
analysis 199
 
1.4%
Other values (1400) 10926
76.9%
2024-07-06T11:24:17.894398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10884
 
10.3%
a 10270
 
9.7%
n 8532
 
8.1%
i 8162
 
7.7%
t 7957
 
7.5%
s 6890
 
6.5%
o 5860
 
5.5%
r 5428
 
5.1%
l 5383
 
5.1%
c 5251
 
5.0%
Other values (34) 31114
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100470
95.0%
Space Separator 5085
 
4.8%
Other Punctuation 111
 
0.1%
Decimal Number 51
 
< 0.1%
Math Symbol 12
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10884
10.8%
a 10270
 
10.2%
n 8532
 
8.5%
i 8162
 
8.1%
t 7957
 
7.9%
s 6890
 
6.9%
o 5860
 
5.8%
r 5428
 
5.4%
l 5383
 
5.4%
c 5251
 
5.2%
Other values (16) 25853
25.7%
Decimal Number
ValueCountFrequency (%)
2 14
27.5%
0 10
19.6%
3 8
15.7%
5 6
11.8%
1 5
 
9.8%
4 3
 
5.9%
8 2
 
3.9%
9 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 92
82.9%
& 7
 
6.3%
# 7
 
6.3%
/ 5
 
4.5%
Space Separator
ValueCountFrequency (%)
5085
100.0%
Math Symbol
ValueCountFrequency (%)
+ 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100470
95.0%
Common 5261
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10884
10.8%
a 10270
 
10.2%
n 8532
 
8.5%
i 8162
 
8.1%
t 7957
 
7.9%
s 6890
 
6.9%
o 5860
 
5.8%
r 5428
 
5.4%
l 5383
 
5.4%
c 5251
 
5.2%
Other values (16) 25853
25.7%
Common
ValueCountFrequency (%)
5085
96.7%
. 92
 
1.7%
2 14
 
0.3%
+ 12
 
0.2%
0 10
 
0.2%
3 8
 
0.2%
& 7
 
0.1%
# 7
 
0.1%
5 6
 
0.1%
1 5
 
0.1%
Other values (8) 15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10884
 
10.3%
a 10270
 
9.7%
n 8532
 
8.1%
i 8162
 
7.7%
t 7957
 
7.5%
s 6890
 
6.5%
o 5860
 
5.5%
r 5428
 
5.1%
l 5383
 
5.1%
c 5251
 
5.0%
Other values (34) 31114
29.4%
Distinct1944
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:18.203557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length30
Mean length11.32074
Min length1

Characters and Unicode

Total characters103381
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1065 ?
Unique (%)11.7%

Sample

1st rowdata science
2nd rowdashboards
3rd rowstatistics
4th rowbusiness intelligence
5th rowadvance excel
ValueCountFrequency (%)
data 578
 
4.2%
management 416
 
3.0%
development 354
 
2.6%
web 328
 
2.4%
javascript 293
 
2.1%
html 225
 
1.6%
research 205
 
1.5%
analysis 182
 
1.3%
sql 177
 
1.3%
machine 167
 
1.2%
Other values (1369) 10925
78.9%
2024-07-06T11:24:18.702774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10966
 
10.6%
a 9575
 
9.3%
n 8217
 
7.9%
t 8099
 
7.8%
i 7936
 
7.7%
s 6847
 
6.6%
o 5951
 
5.8%
r 5670
 
5.5%
c 4834
 
4.7%
l 4820
 
4.7%
Other values (31) 30466
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98463
95.2%
Space Separator 4719
 
4.6%
Other Punctuation 110
 
0.1%
Decimal Number 75
 
0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10966
11.1%
a 9575
 
9.7%
n 8217
 
8.3%
t 8099
 
8.2%
i 7936
 
8.1%
s 6847
 
7.0%
o 5951
 
6.0%
r 5670
 
5.8%
c 4834
 
4.9%
l 4820
 
4.9%
Other values (16) 25548
25.9%
Decimal Number
ValueCountFrequency (%)
2 27
36.0%
0 15
20.0%
3 14
18.7%
1 5
 
6.7%
4 5
 
6.7%
7 3
 
4.0%
5 3
 
4.0%
8 2
 
2.7%
6 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 71
64.5%
# 27
 
24.5%
/ 9
 
8.2%
& 3
 
2.7%
Space Separator
ValueCountFrequency (%)
4719
100.0%
Math Symbol
ValueCountFrequency (%)
+ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98463
95.2%
Common 4918
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10966
11.1%
a 9575
 
9.7%
n 8217
 
8.3%
t 8099
 
8.2%
i 7936
 
8.1%
s 6847
 
7.0%
o 5951
 
6.0%
r 5670
 
5.8%
c 4834
 
4.9%
l 4820
 
4.9%
Other values (16) 25548
25.9%
Common
ValueCountFrequency (%)
4719
96.0%
. 71
 
1.4%
# 27
 
0.5%
2 27
 
0.5%
0 15
 
0.3%
+ 14
 
0.3%
3 14
 
0.3%
/ 9
 
0.2%
1 5
 
0.1%
4 5
 
0.1%
Other values (5) 12
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10966
 
10.6%
a 9575
 
9.3%
n 8217
 
7.9%
t 8099
 
7.8%
i 7936
 
7.7%
s 6847
 
6.6%
o 5951
 
5.8%
r 5670
 
5.5%
c 4834
 
4.7%
l 4820
 
4.7%
Other values (31) 30466
29.5%
Distinct1888
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:19.041247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length78
Median length31
Mean length11.028362
Min length1

Characters and Unicode

Total characters100711
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1040 ?
Unique (%)11.4%

Sample

1st rowdata
2nd rowdata reporting
3rd rowanalytics
4th rowdata management
5th rowanalytical skills
ValueCountFrequency (%)
data 572
 
4.2%
management 468
 
3.5%
development 322
 
2.4%
web 316
 
2.3%
html 245
 
1.8%
javascript 214
 
1.6%
research 205
 
1.5%
sql 191
 
1.4%
software 166
 
1.2%
analytics 162
 
1.2%
Other values (1326) 10677
78.9%
2024-07-06T11:24:19.600016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10521
 
10.4%
a 9165
 
9.1%
n 8208
 
8.2%
t 8097
 
8.0%
i 7636
 
7.6%
s 6839
 
6.8%
o 6157
 
6.1%
r 5600
 
5.6%
c 4518
 
4.5%
l 4436
 
4.4%
Other values (33) 29534
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96094
95.4%
Space Separator 4406
 
4.4%
Other Punctuation 105
 
0.1%
Decimal Number 94
 
0.1%
Math Symbol 11
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10521
10.9%
a 9165
 
9.5%
n 8208
 
8.5%
t 8097
 
8.4%
i 7636
 
7.9%
s 6839
 
7.1%
o 6157
 
6.4%
r 5600
 
5.8%
c 4518
 
4.7%
l 4436
 
4.6%
Other values (16) 24917
25.9%
Decimal Number
ValueCountFrequency (%)
2 30
31.9%
0 15
16.0%
3 12
 
12.8%
4 9
 
9.6%
8 8
 
8.5%
1 8
 
8.5%
5 5
 
5.3%
7 4
 
4.3%
6 2
 
2.1%
9 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 94
89.5%
# 5
 
4.8%
/ 3
 
2.9%
& 3
 
2.9%
Space Separator
ValueCountFrequency (%)
4406
100.0%
Math Symbol
ValueCountFrequency (%)
+ 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96094
95.4%
Common 4617
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10521
10.9%
a 9165
 
9.5%
n 8208
 
8.5%
t 8097
 
8.4%
i 7636
 
7.9%
s 6839
 
7.1%
o 6157
 
6.4%
r 5600
 
5.8%
c 4518
 
4.7%
l 4436
 
4.6%
Other values (16) 24917
25.9%
Common
ValueCountFrequency (%)
4406
95.4%
. 94
 
2.0%
2 30
 
0.6%
0 15
 
0.3%
3 12
 
0.3%
+ 11
 
0.2%
4 9
 
0.2%
8 8
 
0.2%
1 8
 
0.2%
# 5
 
0.1%
Other values (7) 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10521
 
10.4%
a 9165
 
9.1%
n 8208
 
8.2%
t 8097
 
8.0%
i 7636
 
7.6%
s 6839
 
6.8%
o 6157
 
6.1%
r 5600
 
5.6%
c 4518
 
4.5%
l 4436
 
4.4%
Other values (33) 29534
29.3%
Distinct1806
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:19.980633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length28
Mean length10.78767
Min length1

Characters and Unicode

Total characters98513
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique999 ?
Unique (%)10.9%

Sample

1st rowscience
2nd rowadvance excel
3rd rowdata
4th rowdata analysis
5th rowsql
ValueCountFrequency (%)
data 477
 
3.6%
management 406
 
3.1%
development 339
 
2.5%
sql 298
 
2.2%
web 258
 
1.9%
html 237
 
1.8%
analytics 213
 
1.6%
software 193
 
1.5%
business 191
 
1.4%
research 184
 
1.4%
Other values (1261) 10501
79.0%
2024-07-06T11:24:20.424394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10047
 
10.2%
n 8303
 
8.4%
a 8245
 
8.4%
t 7934
 
8.1%
i 7831
 
7.9%
s 6923
 
7.0%
o 6816
 
6.9%
r 5333
 
5.4%
c 4372
 
4.4%
l 4274
 
4.3%
Other values (30) 28435
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94190
95.6%
Space Separator 4166
 
4.2%
Other Punctuation 86
 
0.1%
Decimal Number 49
 
< 0.1%
Math Symbol 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10047
10.7%
n 8303
 
8.8%
a 8245
 
8.8%
t 7934
 
8.4%
i 7831
 
8.3%
s 6923
 
7.4%
o 6816
 
7.2%
r 5333
 
5.7%
c 4372
 
4.6%
l 4274
 
4.5%
Other values (16) 24112
25.6%
Decimal Number
ValueCountFrequency (%)
2 23
46.9%
3 14
28.6%
4 4
 
8.2%
1 2
 
4.1%
7 2
 
4.1%
0 2
 
4.1%
8 1
 
2.0%
5 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 78
90.7%
/ 6
 
7.0%
# 1
 
1.2%
& 1
 
1.2%
Space Separator
ValueCountFrequency (%)
4166
100.0%
Math Symbol
ValueCountFrequency (%)
+ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94190
95.6%
Common 4323
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10047
10.7%
n 8303
 
8.8%
a 8245
 
8.8%
t 7934
 
8.4%
i 7831
 
8.3%
s 6923
 
7.4%
o 6816
 
7.2%
r 5333
 
5.7%
c 4372
 
4.6%
l 4274
 
4.5%
Other values (16) 24112
25.6%
Common
ValueCountFrequency (%)
4166
96.4%
. 78
 
1.8%
2 23
 
0.5%
+ 22
 
0.5%
3 14
 
0.3%
/ 6
 
0.1%
4 4
 
0.1%
1 2
 
< 0.1%
7 2
 
< 0.1%
0 2
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10047
 
10.2%
n 8303
 
8.4%
a 8245
 
8.4%
t 7934
 
8.1%
i 7831
 
7.9%
s 6923
 
7.0%
o 6816
 
6.9%
r 5333
 
5.4%
c 4372
 
4.4%
l 4274
 
4.3%
Other values (30) 28435
28.9%

is_remote
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
0
8878 
1
 
254

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9132
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8878
97.2%
1 254
 
2.8%

Length

2024-07-06T11:24:20.651297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T11:24:20.751856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8878
97.2%
1 254
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 8878
97.2%
1 254
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9132
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8878
97.2%
1 254
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8878
97.2%
1 254
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8878
97.2%
1 254
 
2.8%

norm_review
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70254599
Minimum0
Maximum1
Zeros49
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:20.901751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.625
median0.75
Q30.825
95-th percentile0.975
Maximum1
Range1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.19208394
Coefficient of variation (CV)0.27341119
Kurtosis1.7893354
Mean0.70254599
Median Absolute Deviation (MAD)0.1
Skewness-1.1316837
Sum6415.65
Variance0.036896239
MonotonicityNot monotonic
2024-07-06T11:24:21.096642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.75 1039
 
11.4%
0.775 671
 
7.3%
0.8 584
 
6.4%
0.725 530
 
5.8%
0.7 526
 
5.8%
0.825 477
 
5.2%
0.675 455
 
5.0%
1 437
 
4.8%
0.65 427
 
4.7%
0.85 377
 
4.1%
Other values (31) 3609
39.5%
ValueCountFrequency (%)
0 49
0.5%
0.025 25
0.3%
0.05 34
0.4%
0.075 34
0.4%
0.1 17
 
0.2%
0.125 27
0.3%
0.15 20
0.2%
0.175 29
0.3%
0.2 28
0.3%
0.225 33
0.4%
ValueCountFrequency (%)
1 437
4.8%
0.975 154
 
1.7%
0.95 202
 
2.2%
0.925 263
 
2.9%
0.9 189
 
2.1%
0.875 284
3.1%
0.85 377
4.1%
0.825 477
5.2%
0.8 584
6.4%
0.775 671
7.3%

norm_branches
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
0
7694 
1
1438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9132
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Length

2024-07-06T11:24:21.501061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T11:24:21.640630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9132
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7694
84.3%
1 1438
 
15.7%

norm_max_year
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14750329
Minimum0
Maximum1
Zeros54
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:21.789254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.066666667
Q10.1
median0.13333333
Q30.16666667
95-th percentile0.23333333
Maximum1
Range1
Interquartile range (IQR)0.066666667

Descriptive statistics

Standard deviation0.056260619
Coefficient of variation (CV)0.38141943
Kurtosis9.3456492
Mean0.14750329
Median Absolute Deviation (MAD)0.033333333
Skewness1.1700083
Sum1347
Variance0.0031652572
MonotonicityNot monotonic
2024-07-06T11:24:21.937891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.1666666667 2064
22.6%
0.1 1935
21.2%
0.1333333333 1893
20.7%
0.2 1175
12.9%
0.06666666667 963
10.5%
0.2333333333 910
10.0%
0.2666666667 63
 
0.7%
0 54
 
0.6%
0.3333333333 38
 
0.4%
0.3 11
 
0.1%
Other values (8) 26
 
0.3%
ValueCountFrequency (%)
0 54
 
0.6%
0.06666666667 963
10.5%
0.1 1935
21.2%
0.1333333333 1893
20.7%
0.1666666667 2064
22.6%
0.2 1175
12.9%
0.2333333333 910
10.0%
0.2666666667 63
 
0.7%
0.3 11
 
0.1%
0.3333333333 38
 
0.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.6666666667 2
 
< 0.1%
0.6 1
 
< 0.1%
0.5333333333 1
 
< 0.1%
0.5 9
 
0.1%
0.4333333333 3
 
< 0.1%
0.4 7
 
0.1%
0.3666666667 2
 
< 0.1%
0.3333333333 38
0.4%
0.3 11
 
0.1%

composite_score
Real number (ℝ)

Distinct253
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0075175
Minimum0.066666667
Maximum2.275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:22.088690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.066666667
5-th percentile0.48333333
Q10.78333333
median0.91666667
Q31.0666667
95-th percentile1.95
Maximum2.275
Range2.2083333
Interquartile range (IQR)0.28333333

Descriptive statistics

Standard deviation0.42074486
Coefficient of variation (CV)0.4176055
Kurtosis0.93367673
Mean1.0075175
Median Absolute Deviation (MAD)0.14166667
Skewness1.1503076
Sum9200.65
Variance0.17702624
MonotonicityNot monotonic
2024-07-06T11:24:22.256263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9166666667 245
 
2.7%
0.8833333333 237
 
2.6%
0.85 234
 
2.6%
0.95 195
 
2.1%
0.9083333333 174
 
1.9%
0.9833333333 171
 
1.9%
0.8166666667 169
 
1.9%
0.925 160
 
1.8%
0.9416666667 156
 
1.7%
0.8416666667 156
 
1.7%
Other values (243) 7235
79.2%
ValueCountFrequency (%)
0.06666666667 4
 
< 0.1%
0.09166666667 1
 
< 0.1%
0.1 10
0.1%
0.1166666667 4
 
< 0.1%
0.125 4
 
< 0.1%
0.1333333333 9
0.1%
0.15 8
0.1%
0.1583333333 2
 
< 0.1%
0.1666666667 12
0.1%
0.175 8
0.1%
ValueCountFrequency (%)
2.275 1
 
< 0.1%
2.233333333 10
0.1%
2.208333333 2
 
< 0.1%
2.2 10
0.1%
2.183333333 3
 
< 0.1%
2.175 3
 
< 0.1%
2.166666667 24
0.3%
2.158333333 6
 
0.1%
2.15 3
 
< 0.1%
2.141666667 10
0.1%

Company Size
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Medium
1889 
Very Small
1839 
Large
1835 
Small
1819 
Very Large
1750 

Length

Max length10
Median length6
Mean length7.1719229
Min length5

Characters and Unicode

Total characters65494
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowVery Large
4th rowVery Small
5th rowVery Large

Common Values

ValueCountFrequency (%)
Medium 1889
20.7%
Very Small 1839
20.1%
Large 1835
20.1%
Small 1819
19.9%
Very Large 1750
19.2%

Length

2024-07-06T11:24:22.413236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T11:24:22.571152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
small 3658
28.8%
very 3589
28.2%
large 3585
28.2%
medium 1889
14.8%

Most occurring characters

ValueCountFrequency (%)
e 9063
13.8%
l 7316
11.2%
a 7243
11.1%
r 7174
11.0%
m 5547
8.5%
S 3658
 
5.6%
V 3589
 
5.5%
y 3589
 
5.5%
3589
 
5.5%
L 3585
 
5.5%
Other values (5) 11141
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49184
75.1%
Uppercase Letter 12721
 
19.4%
Space Separator 3589
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9063
18.4%
l 7316
14.9%
a 7243
14.7%
r 7174
14.6%
m 5547
11.3%
y 3589
 
7.3%
g 3585
 
7.3%
d 1889
 
3.8%
i 1889
 
3.8%
u 1889
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
S 3658
28.8%
V 3589
28.2%
L 3585
28.2%
M 1889
14.8%
Space Separator
ValueCountFrequency (%)
3589
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61905
94.5%
Common 3589
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9063
14.6%
l 7316
11.8%
a 7243
11.7%
r 7174
11.6%
m 5547
9.0%
S 3658
5.9%
V 3589
 
5.8%
y 3589
 
5.8%
L 3585
 
5.8%
g 3585
 
5.8%
Other values (4) 7556
12.2%
Common
ValueCountFrequency (%)
3589
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9063
13.8%
l 7316
11.2%
a 7243
11.1%
r 7174
11.0%
m 5547
8.5%
S 3658
 
5.6%
V 3589
 
5.5%
y 3589
 
5.5%
3589
 
5.5%
L 3585
 
5.5%
Other values (5) 11141
17.0%

Job Category
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Developer and Engineering
3396 
Data and Analysis
2427 
Others
1580 
Marketing and Sales
545 
Creative and Design
419 
Other values (3)
765 

Length

Max length30
Median length25
Mean length18.746058
Min length6

Characters and Unicode

Total characters171189
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData and Analysis
2nd rowData and Analysis
3rd rowData and Analysis
4th rowData and Analysis
5th rowData and Analysis

Common Values

ValueCountFrequency (%)
Developer and Engineering 3396
37.2%
Data and Analysis 2427
26.6%
Others 1580
17.3%
Marketing and Sales 545
 
6.0%
Creative and Design 419
 
4.6%
Management and Strategic Roles 411
 
4.5%
Customer Support 259
 
2.8%
Research 95
 
1.0%

Length

2024-07-06T11:24:22.738332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T11:24:22.905824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
and 7198
29.7%
developer 3396
14.0%
engineering 3396
14.0%
data 2427
 
10.0%
analysis 2427
 
10.0%
others 1580
 
6.5%
marketing 545
 
2.3%
sales 545
 
2.3%
creative 419
 
1.7%
design 419
 
1.7%
Other values (6) 1846
 
7.6%

Most occurring characters

ValueCountFrequency (%)
e 23000
13.4%
n 21599
12.6%
a 17316
10.1%
15066
 
8.8%
i 11013
 
6.4%
r 10360
 
6.1%
g 8578
 
5.0%
s 8163
 
4.8%
d 7198
 
4.2%
l 6779
 
4.0%
Other values (18) 42117
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139123
81.3%
Uppercase Letter 17000
 
9.9%
Space Separator 15066
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23000
16.5%
n 21599
15.5%
a 17316
12.4%
i 11013
7.9%
r 10360
7.4%
g 8578
 
6.2%
s 8163
 
5.9%
d 7198
 
5.2%
l 6779
 
4.9%
t 6722
 
4.8%
Other values (9) 18395
13.2%
Uppercase Letter
ValueCountFrequency (%)
D 6242
36.7%
E 3396
20.0%
A 2427
 
14.3%
O 1580
 
9.3%
S 1215
 
7.1%
M 956
 
5.6%
C 678
 
4.0%
R 506
 
3.0%
Space Separator
ValueCountFrequency (%)
15066
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 156123
91.2%
Common 15066
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23000
14.7%
n 21599
13.8%
a 17316
11.1%
i 11013
 
7.1%
r 10360
 
6.6%
g 8578
 
5.5%
s 8163
 
5.2%
d 7198
 
4.6%
l 6779
 
4.3%
t 6722
 
4.3%
Other values (17) 35395
22.7%
Common
ValueCountFrequency (%)
15066
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23000
13.4%
n 21599
12.6%
a 17316
10.1%
15066
 
8.8%
i 11013
 
6.4%
r 10360
 
6.1%
g 8578
 
5.0%
s 8163
 
4.8%
d 7198
 
4.2%
l 6779
 
4.0%
Other values (18) 42117
24.6%
Distinct52
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
2024-07-06T11:24:23.235023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.860053
Min length3

Characters and Unicode

Total characters135702
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowData Analysis
2nd rowData Analysis
3rd rowData Analysis
4th rowData Analysis
5th rowBusiness Intelligence (BI)
ValueCountFrequency (%)
analysis 3378
19.6%
data 3079
17.8%
management 2392
13.9%
database 861
 
5.0%
programming 631
 
3.7%
others 585
 
3.4%
business 529
 
3.1%
intelligence 529
 
3.1%
bi 529
 
3.1%
supply 460
 
2.7%
Other values (63) 4291
24.9%
2024-07-06T11:24:23.641702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20678
15.2%
n 14106
 
10.4%
e 12110
 
8.9%
s 10680
 
7.9%
t 8871
 
6.5%
8132
 
6.0%
i 7391
 
5.4%
l 5277
 
3.9%
g 4801
 
3.5%
r 4564
 
3.4%
Other values (35) 39092
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107885
79.5%
Uppercase Letter 18607
 
13.7%
Space Separator 8132
 
6.0%
Open Punctuation 539
 
0.4%
Close Punctuation 539
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20678
19.2%
n 14106
13.1%
e 12110
11.2%
s 10680
9.9%
t 8871
8.2%
i 7391
 
6.9%
l 5277
 
4.9%
g 4801
 
4.5%
r 4564
 
4.2%
m 4360
 
4.0%
Other values (15) 15047
13.9%
Uppercase Letter
ValueCountFrequency (%)
D 3941
21.2%
A 3502
18.8%
M 3211
17.3%
I 1304
 
7.0%
B 1061
 
5.7%
P 1042
 
5.6%
O 872
 
4.7%
C 857
 
4.6%
S 823
 
4.4%
L 782
 
4.2%
Other values (7) 1212
 
6.5%
Space Separator
ValueCountFrequency (%)
8132
100.0%
Open Punctuation
ValueCountFrequency (%)
( 539
100.0%
Close Punctuation
ValueCountFrequency (%)
) 539
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126492
93.2%
Common 9210
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20678
16.3%
n 14106
11.2%
e 12110
 
9.6%
s 10680
 
8.4%
t 8871
 
7.0%
i 7391
 
5.8%
l 5277
 
4.2%
g 4801
 
3.8%
r 4564
 
3.6%
m 4360
 
3.4%
Other values (32) 33654
26.6%
Common
ValueCountFrequency (%)
8132
88.3%
( 539
 
5.9%
) 539
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20678
15.2%
n 14106
 
10.4%
e 12110
 
8.9%
s 10680
 
7.9%
t 8871
 
6.5%
8132
 
6.0%
i 7391
 
5.4%
l 5277
 
3.9%
g 4801
 
3.5%
r 4564
 
3.4%
Other values (35) 39092
28.8%

Interactions

2024-07-06T11:24:06.673766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:53.715518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.989702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:56.466255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:58.212377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:59.719901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:01.631433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:03.530037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:05.067260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:06.859377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:53.831120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:55.180953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:56.644903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:58.413170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:59.876484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:01.908133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:03.746695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:05.260761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.057600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:53.948583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:55.334303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:56.821109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:58.597843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:00.273557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:02.152885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:03.936495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:05.452225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.166678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.072697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:55.517025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:57.057032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:58.757290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:00.445317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:02.378422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:04.125226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:05.622639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.319521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.190653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:55.680051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:57.280271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:58.920101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:00.639926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:02.571104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:04.311764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:05.802719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.475108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.316708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:55.854393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:57.412492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:59.095675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:00.813076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:02.767370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:04.470729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:05.984234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.612316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.429312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:56.043798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:57.534935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:59.247792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:01.006098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:02.959224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:04.627252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:06.167167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.762769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.576970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:56.194186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:57.737063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:59.416859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:01.219944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:03.141520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:04.771638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:06.333036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:07.901503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:54.833162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:56.330235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:58.032532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:23:59.593797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:01.420280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:03.328826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:04.933225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T11:24:06.505544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-07-06T11:24:08.155950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-06T11:24:08.699835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Job_roleCompanyjob_paylocationreviewJob post lengthmin_yearmax_yearmore_branch+3_branchesdaysskill_1skill_2skill_3skill_4skill_5skill_6skill_7skill_8is_remotenorm_reviewnorm_branchesnorm_max_yearcomposite_scoreCompany SizeJob CategoryPrimary Skill Category
0Data AnalystOutscal Technologies1500000.0Remote4.312031019data analysisdata visualizationtableaupythonsqldata sciencedatascience10.82500.1000000.925000MediumData and AnalysisData Analysis
1Data AnalystRedbus550000.0Bangalore Rural4.31213207sqlmis reportingvlookupdata analysisadvance exceldashboardsdata reportingadvance excel00.82500.1000000.925000MediumData and AnalysisData Analysis
2Data AnalystAmiha Agro350000.0Ahmedabad(Shyamal Cross Road)2.21224315statistical data analysisdata analyticsdata visualizationdata reportingreportingstatisticsanalyticsdata00.30010.1333331.433333Very LargeData and AnalysisData Analysis
3Data AnalystA K Infra Reality Developers237500.0Lucknow3.11224102data visualizationdata extractionstatisticsdata reportinganalyticsbusiness intelligencedata managementdata analysis00.52500.1333330.658333Very SmallData and AnalysisData Analysis
4Data AnalystBolla Management562500.0Hyderabad(Kondapur)5.01226107vba codingpower biadvance excelsqlsqladvance excelanalytical skillssql01.00000.2000001.200000Very LargeData and AnalysisBusiness Intelligence (BI)
5Data AnalystEnvision Software Engineering Pvt.Ltd725000.0Coimbatore3.71227107mistroubleshooting skillsanalytical skillscommunicational skillsclient interactioninterpretationms officeclient00.67500.2333330.908333MediumData and AnalysisData Analysis
6Data AnalystGrowing E-commerce client of Societas Services.750000.0Mumbai (All Areas)(Vikhroli)4.11225314advance exceladvance excelvlookuphlookupmicrosoftadvance exceldata analysisdata00.77510.1666671.941667Very LargeData and AnalysisData Analysis
7Data AnalystConnect India550000.0Bengaluru(HSR Layout)3.23527208pythonfront endtableaureact.jsdatadata analysisenddevelopment00.55000.2333330.783333SmallData and AnalysisData Analysis
8Data AnalystAnnapurna Finance900000.0Bhubaneswar4.112161011adhoc analysispower biadvance excelsqldatabiadhoc testingdata analysis00.77500.2000000.975000LargeData and AnalysisData Analysis
9Data AnalystAmneal Pharmaceuticals537500.0Ahmedabad(Prahlad Nagar)4.343272012billingaccounts payableinvoicingdata analysispayablesaccountingsustainabilityhiring00.82500.2333331.058333LargeData and AnalysisData Analysis
Job_roleCompanyjob_paylocationreviewJob post lengthmin_yearmax_yearmore_branch+3_branchesdaysskill_1skill_2skill_3skill_4skill_5skill_6skill_7skill_8is_remotenorm_reviewnorm_branchesnorm_max_yearcomposite_scoreCompany SizeJob CategoryPrimary Skill Category
9122Software DeveloperSolytics Partners8.796782e+05Pune4.016261030rcamultithreadingweb technologiesweb servicesoopsdata structuresunit testingengineering design00.75000.2000000.950000MediumDeveloper and EngineeringComputer Science
9123Web DeveloperRelay Human Cloud1.127076e+06Ahmedabad4.219251030project managementsqlsqlweb developmentjavascriptphphtmlruby00.80000.1666670.966667LargeDeveloper and EngineeringDatabase Management
9124Chat Support ExecutivePlutonapps8.572058e+05Kolkata2.91614104relationship managementweb technologiesdata scienceinternmachine learningcorporatejavascriptmongodb00.47500.1333330.608333Very SmallCustomer SupportMachine Learning
9125ManagerASK Eva Ask Executive Virtual Assistance2.740776e+05Madurai2.92425104web technologiesretention managementclient retentioncxomanagementretentioncustomer focusclient00.47500.1666670.641667Very SmallCustomer SupportManagement
9126AssociateCommissum1.630115e+05Bengaluru2.91413104remediationchange managementinformation security analystweb technologiescloud servicesinformation securityvulnerabilityvulnerability management00.47500.1000000.575000Very SmallOthersBusiness Intelligence (BI)
9127AssociateCPP Investments6.299904e+05Bengaluru2.919251030analytical skillssidecssweb technologiesdebuggingsenior associate l1javascriptdata structures00.47500.1666670.641667Very SmallOthersData Analysis
9128AssociateCPP Investments5.411625e+05Mumbai2.934131030automationmanual testingweb technologiessenior associate quality assurancedefect trackingtest casessdlctesting00.47500.1000000.575000Very SmallOthersSupply Chain Management
9129AssociateEurofins1.630115e+05Bengaluru3.014131030remediationchange managementinformation security analystweb technologiescloud servicesinformation securityvulnerabilityvulnerability management00.50000.1000000.600000Very SmallOthersBusiness Intelligence (BI)
9130DeveloperVimerse Infotech5.390104e+05Chennai3.531271030cssweb serviceshybrisimpexjspsap erphibernatesap hybris00.62500.2333330.858333SmallDeveloper and EngineeringResearch and Analysis
9131Tech_role/lead_roleAccenture9.581746e+05Pune4.020241026sapapplication developmentsoftware development life cycletroubleshootingsdlcrestweb servicessap erp00.75000.1333330.883333MediumOthersPerformance Management